May 2023 arXiv papers — page 88

Ivan Sedykh, Dmitry Abulkhanov, Nikita Sorokin, Sergey Nikolenko

Code search is an important and well-studied task, but it usually means searching for code by a text query. We argue that using a code snippet (and possibly an error traceback) as a query while looking for bugfixing instructions and code samples is a natural use case not covered by prior art. Moreover, existing datasets use code comments rather than full-tex

Mukesh Jakhar, Ashok Kumar

The search for highly effective and environmentally safe photocatalysts for water splitting and photovoltaic solar cells is essential for renewable solar energy conversion and storage. Based on first principles calculations, we show that novel 2D $\beta$-PdX$_2$ (X = S, Te) monolayer possesses excellent stabilities and great potentials in solar energy conver

Kaichao You, Guo Qin, Anchang Bao, Meng Cao

Convolution-BatchNorm (ConvBN) blocks are integral components in various computer vision tasks and other domains. A ConvBN block can operate in three modes: Train, Eval, and Deploy. While the Train mode is indispensable for training models from scratch, the Eval mode is suitable for transfer learning and beyond, and the Deploy mode is designed for the deploy

Prajnanaswaroopa S

Cayley graphs are graphs on algebraic structures, typically groups or group-like structures. In this paper, we have obtained a few results on Cayley graphs on Cyclic groups, powers of cycles, Cayley graphs on some non-abelian groups, and vertex, edge and total colorings of Cayley graphs on gyrogroups.

Thomas Haettel, Jingyin Huang

Garside groups are combinatorial generalizations of braid groups which enjoy many nice algebraic, geometric, and algorithmic properties. In this article we propose a method for turning the direct product of a group $G$ by $\mathbb{Z}$ into a Garside group, under simple assumptions on $G$. This method gives many new examples of Garside groups, including group

Mukesh Jakhar, Raman Sharma, Ashok Kumar

Two-dimensional (2D) materials have garnered considerable attention as an emerging thermoelectric (TE) material owing to their unique density of state (DOS) near the Fermi level. We investigate the TE performance of Janus $\beta$-PdXY (X/Y=S, Se, Te) monolayer materials as a function of carrier concentration and mid-temperature range (300 to 800 K) by combin

Ömer Faruk Doğan

We define positive Toeplitz operators between weighted harmonic Bloch spaces $b^\infty_\alpha$ on the unit ball of $\mathbb{R}^n$ for the full range of parameter $\alpha\in\mathbb{R}$. We give characterizations of bounded and compact Toeplitz operators taking one weighted harmonic Bloch space into a\-not\-her in terms of Carleson and vanishing Carleson measu

Kirsten A. Banks, Chantel Y. Y. Ho, Sarah L. Martell, Sven Buder

Data-driven analysis methods can help to infer physical properties of red giant stars where "gold-standard" asteroseismic data are not available. The study of optical and infrared spectra of red giant stars with data-driven analyses has revealed that differences in oscillation frequencies and their separations are imprinted in said spectra. This makes it pos

Leon Chemnitz, David Reichenbach, Hani Aldebes, Mariam Naveed

Automatic code generation has recently attracted large attention and is becoming more significant to the software development process. Solutions based on Machine Learning and Artificial Intelligence are being used to increase human and software efficiency in potent and innovative ways. In this paper, we aim to leverage these developments and introduce a nove

Poonam Chauhan, Jaspreet Singh, Ashok Kumar

We report mechanical, optical and thermoelectric properties of recently fabricated Janus BiTeCl monolayer using density functional and semi-classical Boltzmann transport theory. Janus BiTeCl monolayer exhibits a direct bandgap, high carrier mobility (~10$^3$ cm$^2$V$^{-1}$s$^{-1}$) and high optical absorption in the UV-visible region. The mechanical behavior

Amira Guesmi, Ruitian Ding, Muhammad Abdullah Hanif, Ihsen Alouani

Patch-based adversarial attacks were proven to compromise the robustness and reliability of computer vision systems. However, their conspicuous and easily detectable nature challenge their practicality in real-world setting. To address this, recent work has proposed using Generative Adversarial Networks (GANs) to generate naturalistic patches that may not at

Xia Liang, Johan Klarbring, William Baldwin, Zhenzhu Li

Metal halide perovskites have shown extraordinary performance in solar energy conversion technologies. They have been classified as "soft semiconductors" due to their flexible corner-sharing octahedral networks and polymorphous nature. Understanding the local and average structures continues to be challenging for both modelling and experiments. Here, we repo

Stanislav Dereka, Ivan Karpukhin, Maksim Zhdanov, Sergey Kolesnikov

Deep ensembles are capable of achieving state-of-the-art results in classification and out-of-distribution (OOD) detection. However, their effectiveness is limited due to the homogeneity of learned patterns within ensembles. To overcome this issue, our study introduces Saliency Diversified Deep Ensemble (SDDE), a novel approach that promotes diversity among

Yingchun Wang, Jingcai Guo, Yi Liu, Song Guo

Model substructure learning aims to find an invariant network substructure that can have better out-of-distribution (OOD) generalization than the original full structure. Existing works usually search the invariant substructure using modular risk minimization (MRM) with fully exposed out-domain data, which may bring about two drawbacks: 1) Unfairness, due to

Shun Zhang, Haoran Sun, Runze Yu, Hongshenyuan Cui

In this paper, we propose a two-bit reconfigurable intelligent surface (RIS)-aided communication system, which mainly consists of a two-bit RIS, a transmitter and a receiver. A corresponding prototype verification system is designed to perform experimental tests in practical environments. The carrier frequency is set as 3.5GHz, and the RIS array possesses 25

Jaspreet Singh, Ashok Kumar

Highly efficient, environmental friendly and renewable sources of energy are of great need today to combat with increasing energy demands and environmental pollution. In this work, we have investigated the novel 2D allotropes i.e., $\beta$-Te$_2$X (X = S, Se) using first-principles calculations and study their potential applications in light harvesting devic

M. S. Zarricueta Plaza, A. Roman-Lopes, D. Sanmartim

Context: The study of high-mass stars found to be isolated in the field of the Milky Way may help to probe the feasibility of the core-accretion mechanism in the case of massive star formation. The existence of truly isolated stars may efficiently probe the possibility that individual massive stars can be born in isolation. Aims: We observed WR67a (hereafter

Ch. Cobollo, A. J. Guirao, V. Montesinos

E. Oja, T. Viil, and D. Werner showed, in [Totally smooth renormings, Archiv der Mathematik, 112, 3, (2019), 269--281] that a weakly compactly generated Banach space $(X,\|\cdot \|)$ with the property that every linear functional on $X$ has a unique Hahn--Banach extension to the bidual $X^{**}$ (the so-called Phelps' property U in $X^{**}$, also known as the

Bennet Windt, Miguel Bello, Eugene Demler, J. Ignacio Cirac

Motivated by recent cold-atom realisations of matter-wave waveguide QED, we study simple fermionic impurity models and discuss fermionic analogues of several paradigmatic phenomena in quantum optics, including formation of non-trivial bound states, (matter-wave) emission dynamics, and collective dissipation. For a single impurity, we highlight interesting gr

Anilatmaja Aryasomayajula, Arijit Mukherjee

Let $X$ denote a noncompact finite volume hyperbolic Riemann surface of genus $g\geq 2$, with only one puncture at $i\infty$ (identifying $X$ with its universal cover $\mathbb{H}$). Let $\overline{X}:=X\cup\lbrace i\infty\rbrace$ denote the Satake compactification of $X$. Let $\Omega_{\overline{X}}$ denote the cotangent bundle on $\overline{X}$. For $k\gg1$,

Yan Li, Jingbo Sun, Yongzheng Wen, Xiaoyu Xiong

Two-dimensional photonic circuits with high capacity are essential for a wide range of applications in next-generation photonic information technology and optoelectronics. Here we demonstrate a multi-channel spin-dependent photonic device based on a twinning crystal metamaterial. The structural symmetry and material symmetry of the twinning crystal metamater

Jaspreet Singh, Ashok Kumar

In recent years, researchers have manifested their interest in the two-dimensional (2D) mono-elemental materials of group-VI elements because of their excellent optoelectronic, photovoltaic and thermoelectric properties. Despite the intensive recent research efforts, there is still a possibility of novel 2D allotropes of these elements due to their multivale

Sounaka Mishra, Rohini S, Sagar S. Sawant

It is currently an unsolved problem to determine whether a $\triangle$-free planar graph $G$ contains an independent set $A$ such that $G[V_G\setminus A]$ is $2$-choosable. However, in this paper, we take a slightly different approach by relaxing the planarity condition. We prove the $\mathbb{NP}$-completeness of the above decision problem when the graph is

Rainer Spurzem, Albrecht Kamlah

Dense star clusters are spectacular self-gravitating stellar systems in our Galaxy and across the Universe - in many respects. They populate disks and spheroids of galaxies as well as almost every galactic center. In massive elliptical galaxies nuclear clusters harbor supermassive black holes, which might influence the evolution of their host galaxies as a w

Tashi Namgyal, Peter Flach, Raul Santos-Rodriguez

We describe a proof-of-principle implementation of a system for drawing melodies that abstracts away from a note-level input representation via melodic contours. The aim is to allow users to express their musical intentions without requiring prior knowledge of how notes fit together melodiously. Current approaches to controllable melody generation often requ

Ignatios Antoniadis, Karim Benakli

This review aims to provide a very short and pedestrian introduction to some of the basics of extra-dimensional physics. The hope is to facilitate access and to be, in some respects, complementary to the many already existing reviews on phenomenological applications of extra dimensions in our Universe.

Tom Hosking, Hao Tang, Mirella Lapata

We propose a method for unsupervised opinion summarization that encodes sentences from customer reviews into a hierarchical discrete latent space, then identifies common opinions based on the frequency of their encodings. We are able to generate both abstractive summaries by decoding these frequent encodings, and extractive summaries by selecting the sentenc

Yisong Xiao, Aishan Liu, Tianlin Li, Xianglong Liu

Machine learning (ML) systems have achieved remarkable performance across a wide area of applications. However, they frequently exhibit unfair behaviors in sensitive application domains, raising severe fairness concerns. To evaluate and test fairness, engineers often generate individual discriminatory instances to expose unfair behaviors before model deploym

Baorui Ma, Junsheng Zhou, Yu-Shen Liu, Zhizhong Han

Neural signed distance functions (SDFs) have shown remarkable capability in representing geometry with details. However, without signed distance supervision, it is still a challenge to infer SDFs from point clouds or multi-view images using neural networks. In this paper, we claim that gradient consistency in the field, indicated by the parallelism of level

Pooja Jamdagni, Ashok Kumar, Sunita Srivastava, Ravindra Pandey

The highly efficient photocatalytic water splitting to produce clean energy requires novel semiconductor materials to achieve high solar-to-hydrogen energy conversion efficiency. Herein, the photocatalytic properties of anisotropic $\beta$-PtX$_2$ (X=S, Se) and Janus $\beta$-PtSSe monolayers are investigated based on density functional theory. Small cleavage

Deepak Pal, Amit Kumar, Sumit Kumar Upadhyay, Seema Kushwaha

The main aim of this paper is to determine the multiplicative lie algebra structures on the semi-direct product of an abelian group with a group under certain conditions.

Liting Chen, Lu Wang, Hang Dong, Yali Du

The emergence of large language models (LLMs) has substantially influenced natural language processing, demonstrating exceptional results across various tasks. In this study, we employ ``Introspective Tips" to facilitate LLMs in self-optimizing their decision-making. By introspectively examining trajectories, LLM refines its policy by generating succinct and

Vedran Galetić, Alistair Nottle

Trustworthiness of artificially intelligent agents is vital for the acceptance of human-machine teaming in industrial manufacturing environments. Predictable behaviours and explainable (and understandable) rationale allow humans collaborating with (and building) these agents to understand their motivations and therefore validate decisions that are made. To t

Xuanli He, Qiongkai Xu, Jun Wang, Benjamin Rubinstein

Modern NLP models are often trained over large untrusted datasets, raising the potential for a malicious adversary to compromise model behaviour. For instance, backdoors can be implanted through crafting training instances with a specific textual trigger and a target label. This paper posits that backdoor poisoning attacks exhibit \emph{spurious correlation}

Kai Xiong, Xiao Ding, Yixin Cao, Ting Liu

Large Language Models (LLMs) have shown impressive capabilities in various applications, but they still face various inconsistency issues. Existing works primarily focus on the inconsistency issues within a single LLM, while we complementarily explore the inter-consistency among multiple LLMs for collaboration. To examine whether LLMs can collaborate effecti

Francesco Marzioni, Francesco Rasponi, Paolo Piergentili, Riccardo Natali

Cavity optomechanics is a suitable field to explore quantum effects on macroscopic objects and develop quantum technology applications. A perfect control of the laser noise is required to operate the system in such extreme conditions necessary to reach the quantum regime. In this paper, we consider a Fabry-Per\'ot cavity, driven by two laser fields, with two

Manuel González, Javier Pello

We study the complemented subspaces of the $J$-sums of Banach spaces $J(\Phi)$ and $\hat J(\Phi)$ introduced by Bellenot. As an application, we show that, under some conditions, $J(\Phi)$ and $\hat J(\Phi)$ are subprojective, i.e., every closed infinite-dimensional subspace of either of them contains a complemented infinite-dimensional subspace.

Piyush Kumar Garg, Roshni Chakraborty, Srishti Gupta, Sourav Kumar Dandapat

Online social media platforms, such as Twitter, are one of the most valuable sources of information during disaster events. Therefore, humanitarian organizations, government agencies, and volunteers rely on a summary of this information, i.e., tweets, for effective disaster management. Although there are several existing supervised and unsupervised approache

Lisanne Gossel, Mathis Fricke, Dieter Bothe

Chemical reactor networks (CRNs) enable simulations of combustion reactors with detailed chemical kinetics and strongly simplified flow structure. In this paper, the implementation of a reactor component with less idealized flow structure, namely axial dispersion, to the CRN software NetSMOKE is presented. It is shown exemplarily that different flow models c

Ryota Eguchi, Fukuhito Ooshita, Michiko Inoue, Sébastien Tixeuil

In this paper, we revisit the problem of classical \textit{meeting times} of random walks in graphs. In the process that two tokens (called agents) perform random walks on an undirected graph, the meeting times are defined as the expected times until they meet when the two agents are initially located at different vertices. A key feature of the problem is th

Dianzhao Li, Ostap Okhrin

To achieve fully autonomous driving, vehicles must be capable of continuously performing various driving tasks, including lane keeping and car following, both of which are fundamental and well-studied driving ones. However, previous studies have mainly focused on individual tasks, and car following tasks have typically relied on complete leader-follower info

Jingbo Zhang, Xiaoyu Li, Ziyu Wan, Can Wang

Text-driven 3D scene generation is widely applicable to video gaming, film industry, and metaverse applications that have a large demand for 3D scenes. However, existing text-to-3D generation methods are limited to producing 3D objects with simple geometries and dreamlike styles that lack realism. In this work, we present Text2NeRF, which is able to generate

Katrin Fässler, Jiayin Liu, Tuomas Orponen

For $0 \leq s \leq 1$ and $0 \leq t \leq 3$, a set $F \subset \mathbb{R}^{2}$ is called a circular $(s,t)$-Furstenberg set if there exists a family of circles $\mathcal{S}$ of Hausdorff dimension $\dim_{\mathrm{H}} \mathcal{S} \geq t$ such that $$\dim_{\mathrm{H}} (F \cap S) \geq s, \qquad S \in \mathcal{S}.$$ We prove that if $0 \leq t \leq s \leq 1$, then

Dongwei Ye, Weihao Yan, Christoph Brune, Mengwu Guo

Gaussian process regression is widely applied in computational science and engineering for surrogate modeling owning to its kernel-based and probabilistic nature. In this work, we propose a Bayesian approach that integrates the variability of input data into the Gaussian process regression for function and partial differential equation approximation. Leverag

Jincheng Guo, Yanhui Chen, Xiaofeng Wang, Jie Lin

Tsinghua university-Ma Huateng Telescope for Survey (TMTS) aims to discover rapidly evolving transients by monitoring the northern sky. The TMTS catalog is cross-matched with the white dwarf (WD) catalog of Gaia EDR3, and light curves of more than a thousand WD candidates are obtained so far. Among them, the WD TMTS J23450729+5813146 (hereafter J2345) is one

Yan Sun, Li Shen, Shixiang Chen, Liang Ding

In federated learning (FL), a cluster of local clients are chaired under the coordination of the global server and cooperatively train one model with privacy protection. Due to the multiple local updates and the isolated non-iid dataset, clients are prone to overfit into their own optima, which extremely deviates from the global objective and significantly u

Jieun Han, Haneul Yoo, Yoonsu Kim, Junho Myung

The integration of generative AI in the field of education is actively being explored. In particular, ChatGPT has garnered significant interest, offering an opportunity to examine its effectiveness in English as a foreign language (EFL) education. To address this need, we present a novel learning platform called RECIPE (Revising an Essay with ChatGPT on an I

Tashi Namgyal, Alexander Hepburn, Raul Santos-Rodriguez, Valero Laparra

In this study, we investigate the feasibility of utilizing state-of-the-art image perceptual metrics for evaluating audio signals by representing them as spectrograms. The encouraging outcome of the proposed approach is based on the similarity between the neural mechanisms in the auditory and visual pathways. Furthermore, we customise one of the metrics whic

Alexandra Brintrup, George Baryannis, Ashutosh Tiwari, Svetan Ratchev

While the increased use of AI in the manufacturing sector has been widely noted, there is little understanding on the risks that it may raise in a manufacturing organisation. Although various high level frameworks and definitions have been proposed to consolidate potential risks, practitioners struggle with understanding and implementing them. This lack of u

Davide Bilò, Shiri Chechik, Keerti Choudhary, Sarel Cohen

An $f$-edge fault-tolerant distance sensitive oracle ($f$-DSO) with stretch $\sigma \ge 1$ is a data structure that preprocesses a given undirected, unweighted graph $G$ with $n$ vertices and $m$ edges, and a positive integer $f$. When queried with a pair of vertices $s, t$ and a set $F$ of at most $f$ edges, it returns a $\sigma$-approximation of the $s$-$t

Tianshu Yu, Haoyu Gao, Ting-En Lin, Min Yang

Recently, speech-text pre-training methods have shown remarkable success in many speech and natural language processing tasks. However, most previous pre-trained models are usually tailored for one or two specific tasks, but fail to conquer a wide range of speech-text tasks. In addition, existing speech-text pre-training methods fail to explore the contextua

Yu. G. Ignat'ev

Based on the previously formulated mathematical model of a statistical system with scalar interaction of fermions and the theory of gravitational-scalar instability of a cosmological model based on a two-component statistical system of scalarly charged degenerate fermions, a numerical model of the cosmological evolution of gravitational-scalar perturbations

J. Eduardo Méndez-Delgado, César Esteban, Jorge García-Rojas, Kathryn Kreckel

HII regions, ionized nebulae where massive star formation has taken place, exhibit a wealth of emission lines that are the fundamental basis for estimating the chemical composition of the Universe. For more than 80 years, a discrepancy of at least a factor of two between heavy-element abundances derived with collisional excited lines (CELs) and the weaker re

Chenjie Cao, Yunuo Cai, Qiaole Dong, Yikai Wang

This paper introduces LeftRefill, an innovative approach to efficiently harness large Text-to-Image (T2I) diffusion models for reference-guided image synthesis. As the name implies, LeftRefill horizontally stitches reference and target views together as a whole input. The reference image occupies the left side, while the target canvas is positioned on the ri

Siyuan Feng, Ming Tu, Rui Xia, Chuanzeng Huang

Multilingual training is effective in improving low-resource ASR, which may partially be explained by phonetic representation sharing between languages. In end-to-end (E2E) ASR systems, graphemes are often used as basic modeling units, however graphemes may not be ideal for multilingual phonetic sharing. In this paper, we leverage International Phonetic Alph

Victor Medina-Olivares, Stefan Lessmann, Nadja Klein

We propose a novel method for predicting time-to-event in the presence of cure fractions based on flexible survivals models integrated into a deep neural network framework. Our approach allows for non-linear relationships and high-dimensional interactions between covariates and survival and is suitable for large-scale applications. Furthermore, we allow the

Xin-Qi Luo, Zhi-Wei Sun

Let $n$ be a positive integer, and let $A$ be a set of $k\ge 2n-1$ integers. For the restricted sumset $$ S_n(A)=\{a_1+\cdots +a_n:\ a_1,\ldots,a_n\in A,\ \text{and}\ a_i^2\neq a_j^2\ \text{for} \ 1\le i<j\le n\}, $$ by a 2002 result of Liu and Sun we have $$|S_n(A)|\ge (k-1)n-\frac 32n(n-1)+1.$$ In this paper, we determine the structure of $A$ when the lowe

Armand Jordana, Avadesh Meduri, Etienne Arlaud, Justin Carpentier

In robotics, designing robust algorithms in the face of estimation uncertainty is a challenging task. Indeed, controllers often do not consider the estimation uncertainty and only rely on the most likely estimated state. Consequently, sudden changes in the environment or the robot's dynamics can lead to catastrophic behaviors. In this work, we present a risk

Francisco Tenopala-Carmona, Dirk Hertel, Sabina Hillebrandt, Andreas Mischok

The orientation of luminescent molecules in organic light-emitting diodes (OLEDs) strongly influences device performance. However, our understanding of the factors controlling emitter orientation is limited as current measurements only provide ensemble-averaged orientation values. Here, we use single-molecule imaging to measure the transition dipole orientat

Keyu An, Xian Shi, Shiliang Zhang

Recently, recurrent neural network transducer (RNN-T) gains increasing popularity due to its natural streaming capability as well as superior performance. Nevertheless, RNN-T training requires large time and computation resources as RNN-T loss calculation is slow and consumes a lot of memory. Another limitation of RNN-T is that it tends to access more contex

Denis Kislov, Daniel Ofer, Andrey Machnev, Hani Barhom

The propulsion and acceleration of nanoparticles with light have both fundamental and applied significance across many disciplines. Needle-free injection of biomedical nano cargoes into living tissues is among the examples. Here we explore a new physical mechanism of laser-induced particle acceleration, based on abnormal optothermal expansion of mesoporous v

Siyuan Feng, Ming Tu, Rui Xia, Chuanzeng Huang

We improve low-resource ASR by integrating the ideas of multilingual training and self-supervised learning. Concretely, we leverage an International Phonetic Alphabet (IPA) multilingual model to create frame-level pseudo labels for unlabeled speech, and use these pseudo labels to guide hidden-unit BERT (HuBERT) based speech pretraining in a phonetically-info

Gianmassimo Tasinato

Single field models of inflation capable to produce primordial black holes usually require a significant departure from the standard, perturbative slow-roll regime. In fact, in many of these scenarios, the size of the slow-roll parameter $|\eta|$ becomes larger than one during a short phase of inflationary evolution. In order to develop an analytical control

Alexander Nikitin, Letizia Iannucci, Samuel Kaski

Temporally indexed data are essential in a wide range of fields and of interest to machine learning researchers. Time series data, however, are often scarce or highly sensitive, which precludes the sharing of data between researchers and industrial organizations and the application of existing and new data-intensive ML methods. A possible solution to this bo

Qiong Chang, Xiang Li, Xin Xu, Xin Liu

We present a lightweight system for stereo matching through embedded GPUs. It breaks the trade-off between accuracy and processing speed in stereo matching, enabling our embedded system to further improve the matching accuracy while ensuring real-time processing. The main idea of our method is to construct a tiny neural network based on variational auto-enco

Kevin Morand

In a seminal paper, Bacry and L\'evy-Leblond classified kinematical algebras, a class of Lie algebras encoding the symmetries of spacetime. Homogeneous spacetimes (infinitesimally, Klein pairs) associated to these possible kinematics can be partitioned into four families -- riemannian, lorentzian, galilean and carrollian -- based on the type of invariant met

Marie-Noëlle Célérier

In a recent series of papers new exact analytical solutions of the Einstein equations representing interior spacetimes sourced by stationary rigidly rotating cylinders of different kinds of fluids have been displayed, [Phys. Rev. D {\bf 104}, 064040 (2021); J. Math. Phys. {\bf 64}, 022501 (2023); J. Math. Phys. {\bf 64}, 032501 (2023); J. Math. Phys. {\bf 64

Xin Cheng, Yankai Lin, Xiuying Chen, Dongyan Zhao

Pre-trained language models(PLM) have made impressive results in various NLP tasks. It has been revealed that one of the key factors to their success is the parameters of these models implicitly learn all kinds of knowledge during pre-training. However, encoding knowledge implicitly in the model parameters has two fundamental drawbacks. First, the knowledge

Valentino Delle Rose, Luca San Mauro, Andrea Sorbi

We contribute to a recent research program which aims at revisiting the study of the complexity of word problems, a major area of research in combinatorial algebra, through the lens of the theory of computably enumerable equivalence relations (ceers), which has considerably grown in recent times. To pursue our analysis, we rely on the most popular way of ass

Alexander Trost

We provide bounds linear in the rank for the generalized conjugacy diameters, introduced by Kedra, Libman and Martin, for the special linear and symplectic groups defined over the rings of integers of global fields by way of using certain stability considerations familiar from classical algebraic K-theory. This determines the growth rates of these generalize

Nicolas-Domenic Reiter, Andreas Gerhardus, Jonas Wahl, Jakob Runge

When dealing with time series data, causal inference methods often employ structural vector autoregressive (SVAR) processes to model time-evolving random systems. In this work, we rephrase recursive SVAR processes with possible latent component processes as a linear Structural Causal Model (SCM) of stochastic processes on a simple causal graph, the process g

Matteo Ferrante, Furkan Ozcelik, Tommaso Boccato, Rufin VanRullen

Every day, the human brain processes an immense volume of visual information, relying on intricate neural mechanisms to perceive and interpret these stimuli. Recent breakthroughs in functional magnetic resonance imaging (fMRI) have enabled scientists to extract visual information from human brain activity patterns. In this study, we present an innovative met

Amnah Alluqmani, Morgan Harvey, Ziqi Zhang

Visually impaired (VI) people often face challenges when performing everyday tasks and identify shopping for clothes as one of the most challenging. Many engage in online shopping, which eliminates some challenges of physical shopping. However, clothes shopping online suffers from many other limitations and barriers. More research is needed to address these

Yifan Yang, Xiaoyu Yang, Liyong Guo, Zengwei Yao

Neural Transducer and connectionist temporal classification (CTC) are popular end-to-end automatic speech recognition systems. Due to their frame-synchronous design, blank symbols are introduced to address the length mismatch between acoustic frames and output tokens, which might bring redundant computation. Previous studies managed to accelerate the trainin

Li Cai, Yangyu Fan

In this short notes, we consider multiplicities of representations in general algebraic families, especially the upper semi-continuity of homological multiplicities and the locally constancy of Euler-Poincare numbers. This generalizes the main result of Aizenbud-Sayag for unramified twisting families.

Sira Vegas, Sebastian Elbaum

Software engineering techniques are increasingly relying on deep learning approaches to support many software engineering tasks, from bug triaging to code generation. To assess the efficacy of such techniques researchers typically perform controlled experiments. Conducting these experiments, however, is particularly challenging given the complexity of the sp

Li Cai, Yangyu Fan

We consider the variation of spherical characters in families. We formulate conjectures for the rationality and meromorphic property of spherical characters. As an example, we establish these conjectures in the unitary Gan-Gross-Prasad case.

Shibo Hao, Tianyang Liu, Zhen Wang, Zhiting Hu

Augmenting large language models (LLMs) with external tools has emerged as a promising approach to solving complex problems. However, traditional methods, which finetune LLMs with tool demonstration data, can be both costly and restricted to a predefined set of tools. Recent in-context learning paradigm alleviates these issues, but the limited context length

Yingqiang Gao, Jessica Lam, Nianlong Gu, Richard H. R. Hahnloser

The abstracts of scientific papers consist of premises and conclusions. Structured abstracts explicitly highlight the conclusion sentences, whereas non-structured abstracts may have conclusion sentences at uncertain positions. This implicit nature of conclusion positions makes the automatic segmentation of scientific abstracts into premises and conclusions a

Marco Livesu

We present Advancing Front Mapping (AFM), a provably robust algorithm for the computation of surface mappings to simple base domains. Given an input mesh and a convex or star-shaped target domain, AFM installs a (possibly refined) version of the input connectivity into the target shape, generating a piece-wise linear mapping between them. The algorithm is in

C. S. Davies, F. G. N. Fennema, A. Tsukamoto, I. Razdolski

The Barnett effect, discovered more than a century ago, describes how an inertial body with otherwise zero net magnetic moment acquires spontaneous magnetization when mechanically spinning. Breakthrough experiments have recently shown that an ultrashort laser pulse destroys the magnetization of an ordered ferromagnet within hundreds of femtoseconds, with the

David Stap, Vlad Niculae, Christof Monz

We argue that translation quality alone is not a sufficient metric for measuring knowledge transfer in multilingual neural machine translation. To support this claim, we introduce Representational Transfer Potential (RTP), which measures representational similarities between languages. We show that RTP can measure both positive and negative transfer (interfe

Pouya Agheli, Nikolaos Pappas, Marios Kountouris

The problem of goal-oriented semantic filtering and timely source coding in multiuser communication systems is considered here. We study a distributed monitoring system in which multiple information sources, each observing a physical process, provide status update packets to multiple monitors having heterogeneous goals. Two semantic filtering schemes are fir

Xi Yang, Hang Li, Qinghua Guo, J. Andrew Zhang

In this work, we study sensing-aided uplink transmission in an integrated sensing and communication (ISAC) vehicular network with the use of orthogonal time frequency space (OTFS) modulation. To exploit sensing parameters for improving uplink communications, the parameters must be first associated with the transmitters, which is a challenging task. We propos

Nick Dewaele, Nick Vannieuwenhoven

Many numerical problems with input $x$ and output $y$ can be formulated as a system of equations $F(x, y) = 0$ where the goal is to solve for $y$. The condition number measures the change of $y$ for small perturbations to $x$. From this numerical problem, one can derive a (typically underdetermined) relaxation by omitting any number of equations from $F$. We

Suryakanta Mishra, Keerthana, Krishna Rudrapal, Biswajit Jana

Identification of novel multiferroic materials with high-ordering temperatures remains at the forefront of condensed matter physics research. In this regard, the antiferromagnetic RCrO$_3$ compounds (like GdCrO$_3$) constitute a promising class of multiferroic compounds, which, however, mostly become ferroelectric concomitant with the antiferromagnetic order

D. E. Morosan, J. Pomoell, A. Kumari, E. K. J. Kilpua

The Sun produces the most powerful explosions in the solar system, solar flares, that can also be accompanied by large eruptions of magnetised plasma, coronal mass ejections (CMEs). These processes can accelerate electron beams up to relativistic energies through magnetic reconnection processes during solar flares and CME-driven shocks. Energetic electron be

Vidar Gudmundsson, Vram Mughnetsyan, Nzar Rauf Abdullah, Chi-Shung Tang

We use a recently proposed quantum electrodynamical density functional theory (QEDFT) functional in a real-time excitation calculation for a two-dimensional electron gas in a square array of quantum dots in an external constant perpendicular magnetic field to model the influence of cavity photons on the excitation spectra of the system. The excitation is gen

Fanyu Wang, Zhenping Xie

As the foundation of current natural language processing methods, pre-trained language model has achieved excellent performance. However, the black-box structure of the deep neural network in pre-trained language models seriously limits the interpretability of the language modeling process. After revisiting the coupled requirement of deep neural representati

Hung-Cheng Wu, Ai Nakamura, Daisuke Okuyama, Kazuhiro Nawa

Neutron and x-ray diffraction experiments were performed on the ternary intermetallic compound YbNiSn, formerly categorized as a ferromagnetic Kondo compound. At zero field, an increase in scattering intensity was observed on top of allowed and forbidden nuclear reflections below Tc, breaking the reflection condition of the crystal symmetry Pnma. This indica

Fangkai Yang, Pu Zhao, Zezhong Wang, Lu Wang

Large Language Model (LLM) has gained popularity and achieved remarkable results in open-domain tasks, but its performance in real industrial domain-specific scenarios is average due to its lack of specific domain knowledge. This issue has attracted widespread attention, but there are few relevant benchmarks available. In this paper, we provide a benchmark Q

Jinyi Hu, Xu Han, Xiaoyuan Yi, Yutong Chen

Diffusion models have made impressive progress in text-to-image synthesis. However, training such large-scale models (e.g. Stable Diffusion), from scratch requires high computational costs and massive high-quality text-image pairs, which becomes unaffordable in other languages. To handle this challenge, we propose IAP, a simple but effective method to transf

Zengwei Yao, Wei Kang, Fangjun Kuang, Liyong Guo

Connectionist Temporal Classification (CTC) suffers from the latency problem when applied to streaming models. We argue that in CTC lattice, the alignments that can access more future context are preferred during training, thereby leading to higher symbol delay. In this work we propose the delay-penalized CTC which is augmented with latency penalty regulariz

Xing-Hua Yang, Fei Huang, Ji Xu

We investigate the neutral-current neutrino-nucleon deep inelastic scattering with particular emphasis on short-range correlation and EMC effect, as well as their impact on the weak-mixing angle $\sin^2\theta_W$ determination. The ratios of structure function $F_{2(NC)}^{A}$ and $x F_{3(NC)}^{A}$ are presented where the nuclei $A$ are chosen as carbon, iron

Asadullah Tariq, Mohamed Adel Serhani, Farag Sallabi, Tariq Qayyum

Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive ove

Piyush Kumar Garg, Roshni Chakraborty, Sourav Kumar Dandapat

Disaster summarization approaches provide an overview of the important information posted during disaster events on social media platforms, such as, Twitter. However, the type of information posted significantly varies across disasters depending on several factors like the location, type, severity, etc. Verification of the effectiveness of disaster summariza

James Renshaw, William Warhurst

In 1995 Grillet introduced the concept of a stratified semigroup as a kind of generalisation of finite nilsemigroups. We extend these ideas here by allowing a more general Base and describe them in terms of extensions of semigroups by stratified semigroups. We consider semillatices of certain types of group-bound semigroups and also semillatices of Clifford

Ali Ismail-Fawaz, Angus Dempster, Chang Wei Tan, Matthieu Herrmann

The measurement of progress using benchmarks evaluations is ubiquitous in computer science and machine learning. However, common approaches to analyzing and presenting the results of benchmark comparisons of multiple algorithms over multiple datasets, such as the critical difference diagram introduced by Dem\v{s}ar (2006), have important shortcomings and, we

Louise Dennis, Marie Farrell, Michael Fisher

In this short position paper we highlight our ongoing work on verifiable heterogeneous multi-agent systems and, in particular, the complex (and often non-functional) issues that impact the choice of structure within each agent.

Mingle Xu, Hyongsuk Kim, Jucheng Yang, Alvaro Fuentes

Recent advancements in deep learning have brought significant improvements to plant disease recognition. However, achieving satisfactory performance often requires high-quality training datasets, which are challenging and expensive to collect. Consequently, the practical application of current deep learning-based methods in real-world scenarios is hindered b